CHAPTER 6

EMPIRICAL ANALYSIS

6.1 Graphical and visual exploration

Graphical plots of both the variables- Exchange Rate and share prices

represented by SENSEX were made for the whole period(July1991to

June2006)separately and combined and for various time buckets from various

perspectives to visually explore the linkage, if any, between them and are

illustrated in the following sections. The major movements in the exchange rate

and SENSEX were tracked against the various episodes which appear to have

impacted change.

6.1.1 Graphical plot and episodic analysis of the behaviour of the

Exchange Rate:

A graphical plot of the daily exchange rate, measured in terms of the

daily rate1 of US Dollar per rupee for the period July 1991- June 2006 was

made to identify the major variations and trends in the value of rupee over the

period and is presented below .

118

1 Based on RBI reference rate (spot ) of US Dollar to the INR

Chart 6.1.1DAILY EXCHANGE RATE (DOLLAR PER RUPEE)

July 1991 - June2006

00.005

0.010.0150.02

0.025

0.030.0350.04

0.0450.05

7/1/

1991

7/1/

1992

7/1/

1993

7/1/

1994

7/1/

1995

7/1/

1996

7/1/

1997

7/1/

1998

7/1/

1999

7/1/

2000

7/1/

2001

7/1/

2002

7/1/

2003

7/1/

2004

7/1/

2005

Dol

lar P

er ru

pee

Source: Data analysis - drawn by taking the inverse of the daily exchange rates obtained from RBI publications.

Chart 6.1.1. depicts the dollar parity of rupee. The power of the rupee

measured in terms dollar per rupee, shows a steady decline up to 2001. The

exchange rate movement is punctuated by abrupt and sharp changes representing

currency depreciations in March 92, March 93, Sept 95, April 96 etc. and

characterized by long periods of placidity broken by sporadic changes which is

more of a practical illustration of what is described by the “Overshooting

Model“2. The period 2001 to 2006 appeared to be more calm and moderated by

market forces and was free from any momentous happenings.

119

2 Rudiger Dornbusch's masterpiece, "Expectations and Exchange Rate Dynamics" in the Journal of Political Economy, in 1976

6.1.2 An episodic analysis reveals the following.

Chart 6.1.2 a Rupees per Dollar

20

22

24

26

28

30

32

34

36

a) The impact of the initial devaluation of the rupee in June - July 1991

continued for a while. The fluctuations were

rampant during the dual exchange rate regime

between March 1992 to March 1993. With the

unification of the exchange rate from March

1993, the exchange rate became market

determined and it settled down around near

constant parity level of Rs. 31.37, for prolonged phase of period up to July

1995 .

b) There was upward pressure on the exchange rate supported by surges of

capital inflow during 1993-95 and the first half

of 1995-96, coupled with robust export growth.

India adopted the current account convertibility

in August 1994 while accepting Article VIII of

IMF agreement. During August 1995 to June

1996 the rupee displayed high volatility. The

monetary and other measures like RBI intervention succeeded in restoring

orderly conditions and the rupee traded in the range of Rs 34-35 per US Dollar

over the period March to June 1996. Between July 1996 and August 1997, the

rupee remained very stable (at around Rs 35 to the dollar).

Chart 6.1.2bRupees per Dollar

31

33

35

37

39

41

9/1/

1995

1/1/

1996

5/1/

1996

9/1/

1996

1/1/

1997

5/1/

1997

9/1/

1997

120

c) August 1997 to June 1998 (the Asian Currency Crisis period) the

rupee was subjected to renewed volatility and the

rupee depreciated from Rs 35.7 to 42.5 against

the US Dollar.

Chart 6.1.2cRupee per Dollar

37

39

41

43

45

47

49

5/1/

1998

5/1/

1999

5/1/

2000

5/1/

2001

5/1/

2002

d) The trend continued although with

some resilience and by the end of December

2000 the exchange rate reached Rs 46.75 to a US

dollar and by May 2002 it became the weakest

ever in the history of Rupee at Rs. 49.06 to a

US Dollar.

e) In May 2004 RBI introduced the Market Stabilization Scheme to

absorb excess liquidity and inject liquidity whenever needed. Since that time

there have been signs of recovery and the daily volatility remained range bound.

6.1.3 Exchange Rate Volatility. In the graph below the volatility of the rupee

is plotted in terms of the daily rate of change(%) in the Rupee - Dollar parity

121

Chart 6.1.3VOLATILITY OF EXCHANGE RATE 1991-2006

-0.080

-0.060

-0.040

-0.020

0.000

0.020

0.040

0.060

0.080

7/4/

1991

7/4/

1992

7/4/

1993

7/4/

1994

7/4/

1995

7/4/

1996

7/4/

1997

7/4/

1998

7/4/

1999

7/4/

2000

7/4/

2001

7/4/

2002

7/4/

2003

7/4/

2004

7/4/

2005

Source: Drawn based on exchange rate data compiled from RBI publications

Chart 6.1.3 displays several spikes of high volatility in the exchange rate and

intermittent periods of calm. The major spike are in 1992/993, 1995/1996,

1997/1998, 2000 and 2001.

6.1.4 Volatility of SENSEX (1991-2006) . In the following chart the volatility

of SENSEX is portrayed by plotting the rate of change in the daily closing

price .

Chart 6.1.4VOLATILITY OF SENSEX 1991-2006

-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

0.250

Source: Drawn based on exchange rate data compiled from BSE publications

122

6.1.5 Relative volatility compared. The following chart shows the daily Rate

of change in exchange rates as embedded in the rate of change in SENSEX.

Chart 6.1.5RELATIVE DAILY VOLATILITY

Exchange Rate Vs SENSEX(1991-2006)

-0.1500-0.1000-0.05000.00000.05000.10000.15000.20000.2500

SENSEX EXR

Source : Data Analysis (Base data from BSE and RBI publications)

With reference to chart 6.1.5 above , the rate of change in SENSEX

appeared to be much greater when compared to that of the exchange rate. The

bouts of volatility depicted at certain junctures and the comparative patterns

displayed by the two variables imply that the exchange rate shocks may have

some impact on the price of shares.

123

6.1.6 The Time Cycle – The Structural Breaks – The Three Phases

Three broad phases are identified for the purpose of analysis This is in

accordance with the typical behaviour of the Indian rupee vis-à-vis the US Dollar

during the period and the nature of the foreign exchange rate management regime

prevalent in the country during the past fifteen years after the liberalization process

was ushered in.

i. Phase 1 : period covering July 1991 to July1995.

ii. Phase 2 : period covering August 1995 to Dec2000.

iii. Phase 3 : period covering Jan 2001 to June 2006.

The Exchange Rate expressed in terms of Dollar per Rupee in the three phases is

depicted in the following charts.-

Source: Data analysis.(Dollar per rupee value derived based on daily exchange rate data from RBI publications and website)

124

Phase 1. After the balance of payment crisis of 1991 a two-step downward

adjustment in the exchange rate was undertaken in July 1991 which was then

followed by a transitional period of dual exchange rates for eleven months, before a

market-determined exchange rate system was put in place in March, 1993. The

Rupee remained least volatile during this short period. From a ‘controlled regime’

the market was brought under ‘managed float.’ Since then, exchange rate is largely

determined by demand and supply conditions in the market. During 1994-95 there

was upward pressure on the rupee because of foreign portfolio capital inflow that

was allowed to enter India. The market was not freely allowed to determine the

exchange rate of the rupee. The RBI intervened to maintain the nominal value of

the rupee at a constant level of Rs 31.4/ 1US $ for a period of sixteen months

from March 1993 to July 1995. This was due to the conflict between the objectives

of export promotion and the free movement of the rupee.

Phase 2 From August 1995 to May 96 the rupee was subjected to

rampant fluctuations, and it varied between 34.25 to 37.94 to a Dollar after

which it regained strength and remained stable at around Rs 35 to a dollar until

about August 1997. The bouts of currency turmoil and contagious financial

crises in the East Asian countries had some effect, although limited, on the

Rupee-dollar parity and the rupee was subjected to renewed volatility and

depreciation between August 1997 to June 1998. Thus Rupee was at its

maximum volatility during the (Phase2) period.

125

Table. 6.1.7

Summary statistics on Exchange Rate : Dollar per Rupee

Phase 1 Phase 2 Phase 3

Jly1991-

Jly1995

Aug1995-

Dec2000

Jan 2001-

Jun2006

Mean 0.0332 0.0255 0.0217

Range 0.0135 0.0105 0.0027

Standard Deviation 0.0025 0.0027 0.0008

10.51% Coefft. Of Variation 7.56% 3.65%

Source : Data Analysis: Descriptive Statistics based on data from RBI and BSE publications : Details in soft copy in the CD- ROM

Phase 3: According to RBI the exchange rate policy in recent years (Phase 3)

has been guided by the broad principles of careful monitoring and management

of exchange rates with flexibility, without a fixed target or a pre-announced

target or a band, while allowing the underlying demand and supply conditions to

determine the exchange rate movements over the period in an orderly way.

The determinants of exchange rate, however, seemed to have altered

dramatically over the period. Earlier, factors related to changes in merchandise

trade flows and the behaviour of commodity price inflation were well

understood. This provided guidance for operating monetary policy principally

targeting low inflation which was consistent with exchange rate changes under

purchasing power parity. These traditional anchors appear to have been swept

126

away by the vicissitudes of capital movements, with currencies often moving far

out of alignment of the traditional fundamentals. Moreover, it appears that

expectations and even momentary reactions to the day’s news are often more

important in determining fluctuations in capital flows and hence it serves to

amplify exchange rates volatility. There appears to be an interplay of both the

goods market approach and the portfolio balancing theory. The globalisation of

financial markets, even though imperfect, has now magnified the impact of

capital flows on the determination of exchange rate with a spillover effect to or

from the equity market. Low interest rates in the US have encouraged capital to

flow into emerging market economies. This has resulted in a large build-up of

foreign exchange reserves and abundant domestic liquidity.

6. 2 The Indian Equity Market and Share prices.

The Indian equity market went through several phases in its transition

during the economic transformation of the country. It went through several

cycles of ups and downs and sectoral shifts and is emerging as a globally

integrated and relevant market. The SENSEX ( the thirty scrip- benchmark

index of BSE)has grown ten fold from 1200 range in 1991 to over 12000+ in

May 2006 and seems to trend upward in the future.

127

SENSEX (1991-2006)Chart 6.2.1

0

2000

4000

6000

8000

10000

12000

14000

Source data from published information of Mumbai Stock Exchange.

6.2.1. Making Sense out of SENSEX - an episodic analysis of the three

phases

The movement of the SENSEX during the period has been almost a

roller coaster ride. While supported by the baseline of economic fundamentals it

was also driven by sentiments of local investors and more often led by FII

actions . Neither the flow oriented theory nor the portfolio balance model seem

to have its exclusive dominance on the behaviour of SENSEX. It has recorded

new levels of valuations since 2002 and as it scaled new heights, it seemed to be

on a different growth trajectory compared to that of the 1990s.

128

CHART 6.2.2 a SENSEX PHASE 1

1000

1500

2000

2500

3000

3500

4000

4500

5000

Phase 1 . Hand in hand with the economic reforms, the equity market also

underwent a lot of reformation. From a stage of no position in anywhere on the

global investment map, the amateur race of economic recovery in the first phase

did attract the attention of the foreign investors . In the early half of the 1990s,

foreign investment in India, like in other

emerging economies, was considered a ‘frontier

asset class’ by the large global investors who

stepped into once in a while to add some extra

marginal increase in their overall returns. The

1992 securities market scam in India further

highlighted the extreme dangers and acted as a

deterrent. In the years 1994 and 1995, India got its first taste of significant

foreign capital flows with GDRs and IPOs mopping up over US $ 4bn from

foreign investors. This was far surpassing the government’s expectations that FII

inflows in any given year would be in the region of a few hundred million

dollars. The market was set on positive sentiments which aroused interest from

domestic institutional and retail investors. All the regional stock exchanges

became very active and trading volume substantially increased.

129

CHART 6.2.2.bSENSEX PHASE 2

1000

2000

3000

4000

5000

6000

7000

The Second Phase was the crisis phase mooted by the Asian currency crisis

and the shock waves had their tsunamic effect on both the currency and the

stock markets. The investment mood was completely shattered by July 1997

with the onset of what is known as the Asian meltdown. India, with significant

capital controls, was hurt to a small extent by the

global turmoil but, did join the meltdown brigade

although at a lesser pace. Under this scenario

even the relatively better fundamentals of the

individual companies also could not enthuse the

market. The recessionary trends in the global

economy set an apathetic mood in the market.

Foreign investors, looking for reasons not to invest in India, found plenty.

Seeing both the stock market indices and the rupee tumbling down one was

tempted to believe the strong association these two economic variables may

have. And many a research reports were produced on this.

The nuclear test in Pokhran in 1998 and the Kargil conflict in 1999

coupled with a payment crisis on the BSE in 1998, continued to rock the equity

markets and shattered the confidence of domestic investors in equity markets.

This prompted them to think that government bonds and bank accounts were

better places to park their money.

130

There were major shifts in the sector-wise make up of the Indian equity market

in terms of Turnover and Market capitalization.(Refer table 6.2 below)

Table 6.2

Sector wise (%) representation of top 50 companies in Turnover and

Market Capitalisation at NSE

Turnover Market Capitalisation

1995 -

1996

2000 -

2001

2005 –

2006

1995-

1996

2000-

2001

2005-

2006

Manufacturing 79.3 % 9.85% 43.01% 62.05% 20.79% 29.30%

Financial Services 17.25 % 1.39% 18.15% 11.41% 7.84% 10..07%

FMCG 1.12 % 2.56% 4.57% 10.07% 17.30% 7.18%

IT 0.00 % 75.56% 21.49% 0.00% 22.80% 23.97%

Pharmaceuticals 0.25 % 1.66% 2.43% 1.85% 4.52% 4.05%

Others 2.09 % 8.98% 10.35% 14.62% 26.76% 25.43%

Total 100.00 100 100 100 100 100

Source :NSE Fact book 2005 & 2006

By March 2000, with the dot.com bubble burst, India, and the equity

market were also circumvent with the negative effects of an adverse global

environment. This coupled with more scams of its own giving, foreign and

domestic investors gave more reason to stay away from the Indian stock market.

In the global meltdown, India was unable to differentiate itself from the scandals

131

surrounding Enron, World Com. etc.

The period 1996- 2000 marked as Phase two in this study, was thus a shaky

period for the Indian Equity Market. Moreover, the formation of the Euro Zone

redirected global investors to potential areas within that zone rather than the post

crisis Asian countries.

CHART 6.2.2c SENSEX PHASE 3

1000

3000

5000

7000

9000

11000

13000

Phase 3 (2001-2006), is a period which has seen the upswing and the downslide

of both Foreign Exchange and Equity markets. After a long lull during 2001-

2002 there was broad based rally in share prices in 2003.The market exhibited

renewed confidence in the fundamentals of the

country .In the equity market there was

significant support by FIIs and domestic mutual

funds. SENSEX marched towards dizzying

heights recording substantial investment by

institutional investors (Rs10.4 billion by FIIs and

Rs42.04 bn. new fund offers by mutual funds in 2005) and it appeared that the

momentum has been set. The faster pace in the modernization of the market in

its conduct and structure viz. dematerialization, on-line trading, diminishing

transaction costs, rolling settlement from transaction(T) day plus 5 days(T +5)

in 2000 to T+2 days in 2005 etc and the proactive role of SEBI the market

regulator etc., the Indian market is set on a consistently increasing scale and

pace of activity. Market Maturity measured by the MCap Ratio3 is more than

3 Market Capitalisation ratio

132

1:1 with Market Capitalisation4 surpassing the GDP(May 2006) is pushing the

Indian capital market ahead to be on the big league of the global best .

On the foreign currency market, the rupee strengthened on account of

both exogenous and indigenous reasons . A combined chart of the power of the

rupee measured in terms of the Dollar per rupee and power of the equity market

measured by the daily SENSEX is depicted below.

Chart 6.2.3SENSEX Vs US Dollar per Rupee

2001-2006

0

2000

4000

6000

8000

10000

12000

14000

SENS

EX

0.019

0.0195

0.02

0.0205

0.021

0.0215

0.022

0.0225

0.023

0.0235

US D

olla

r per

Rup

eeSENSEX Ex Rate Linear (SENSEX) Linear (Ex Rate)

Source: Data analysis - Chart drawn based on daily data on SENSEX(BSE) and Exchange rate

(RBI)converted into Dollar per Rupee.

The graph 6.2.3 indicates some similarity in the behaviour of the two

variables during the period (2001- 2006) as they trend toward the same

direction. The apparent co- movement and the cyclical pattern tend to support

the view that “stock price index and exchange rate levels show a common cycle

which are fundamentally short run in nature”5

4 (Source: NSE Fact book 2005 & 2006) .

5 Morely and Pentecosr(2000) 133

Both variables are trending upwards suggesting that a strong rupee is

supported by a vibrant stock market or is it vice-versa. Does it suggest any

causal relationship? Are the two markets integrating?

So the puzzle remains as to whether the goods market approach or the

stock oriented approach is more relevant in case of India . Or is it the

monetarists’ approach which is prevalent?

6.3. Special effects from a focused perspective

6.3.1 BSE indices Vs DOLLEX . More broad based indices like the BSE

200, BSE 100 were taken and compared against their corresponding Dollar

series. Charts (6.3.1 a for Dollex 200 Vs BSE 200 and 6.3.1.b Dollex 100 Vs

BSE 100 were prepared for phase 3.

Source: Data Analysis -drawn based on the published information of BSE and www.bseindia.com

By structure and definition the Dollex is constructed from the rupee

based index multiplied by a price relative of the Dollar – Rupee Exchange rate

DOLLEX 200

134

Any disproportionality in the gap between the BSE index and its corresponding

Dollex series can only be attributed to the effect of exchange rate fluctuations as

by construction each pair has the same set of underlying shares.

A cursory glance at the above charts would suggest that the same

parities were not maintained all through and some impact of exchange rate

fluctuation is prevailing.

The daily rate of change in the pairs of data was taken and plotted as

in charts 6.3.2.a and 6.3.2 b. A magnified version of the graph revealed traces of

difference in daily rate of change for the data sets.

Source: DATA ANALYSIS : drawn based on daily rate of change derived from source data from BSE Publications

135

The data sets were further subjected to t test, to check if such variations were

statistically significant. The test results are presented in table 6.3.1 below.

Source : Data Analysis : detailed test result in the soft copy on CD ROM

The above test statistics reveal that the traces of differences in the

rate of change in the BSE series and DOLLEX SERIES are statistically not

significant. This leads to the conclusion that exchange rates fluctuations have No

significant effect on the Share prices, measured by gross indices like SENSEX,

BSE indices ,and Dollex series. However the Degree of correlation and hence

the coefficient of determination varied between the different pairs.

TABLE 6.3.1

t Test results for mean daily rate of change for like pairs of indices

MEAN Rate

of change t statistic Probability

Critical

value @5 %

SENSEX 30 0.00079

DOLLEX 30 0.00080 0.19225 0.84757

BSE 100 0.00079

DOLLEX 100 0.00081 0.04304 0.96567

BSE 200 0.00087

DOLLEX 200 0.00085 0.03080 0.75810

1.962

136

TABLE 6.3.2

Correlation : Dollex Vs BSE series

(Based on the Rate of change in daily prices)

Pearson's Coefft

of Correlation

Coefft. Of

determination

SENSEX 30 Vs DOLLEX 30

Between Rate of change in prices

Between Prices

0.9717

(0.9987)

94.4%

(99.7%)

BSE 100 Vs DOLLEX100

Between Rate of change in prices

Between Prices

0.43370

(0.9974)

18.8%

(99.47%)

BSE 200 Vs DOLLEX 200

Between Rate of change in prices 0.9894 97.9%

(0.9990) Between Prices

Source : Data Analysis : Detailed test results in the soft copy on CD ROM

(99.7%)

The above findings are intriguing and call for a thorough search. The

next section 6.4 of the Data analysis concentrates on the search for further

empirical evidence using more sophisticated tools of time series analysis.

137

6.4 Time Series Analysis

6.4.1 Data Sets : As mentioned earlier, daily exchange rate expressed in terms

of Indian rupee per U.S Dollar and the stock prices represented by daily closing

prices were the key variables used in the study and were subject to different level

of analysis. Three levels were identified .viz. i) at the first level with an overall

market index represented by SENSEX (the thirty scrip benchmark BSE index) ii)

the sectoral indices (representing subgroups of different industry sectors) at the

second level and (iii) the share prices of individual companies as the micro level.

Pairs of data sets for the different periods were chosen as below and subjected to

further analysis using time series models.

Table 6.4.1

Period Data Set

A 1991 -2006 Exchange rate (Rupee per Dollar ) EXR 9106

Vs SENSEX

B 2000 – 2006 Exchange rate (Rupee per Dollar ) EXR 20-06

Vs Selected company share prices

Import companies , export companies

C 2001 – 2006 Exchange rate (Rupee per Dollar ) EXR 2106

Vs Sectoral indices- BSE MID CAP& Small cap

and13 industry sectors Dollex series

BSE 100, BSE 200,BSE 500

D Special Time Zones Exchange Rate and the SENSEX

138

Going by the literature available it was felt that the impact of the

exchange rate on share prices could better be studied by investigating the response

of share prices to a shock or change in the exchange rate in terms of either

temporary and/or a permanent shift in the equilibrium level. Theory postulates

that if the response of a variable to a shock in the other has an infinite memory, a

long run relationship is said to exist between the variables. On the other hand when

the response of one variable to a shock in the other variable is temporary or

reversible in nature a short term relationship is said to exist between them.

Recent developments in econometrics have offered sophisticated testing

tools, such as Johansen’s Co-integration test and Granger Causality tests on the

time series which can testify the presence and impact, if any, of such relationship

between Exchange Rates and Share prices. The theoretical background for the

same is described in chapter 5 on Research methodology of this thesis. As

described in that chapter, as a first step all the data series were Log transformed and

checked for stationarity. The ADF and /or Phillip - Peron Unit Root Test were

applied to find out the stationary nature of the series and the further steps for

co-integration test and causality applied in the sequence depicted in the Flow Chart

(Chart 6.4.3). The tests were repeated for data sets for each sub period at varying

scope and scale as indicated in table 6.4.1. Software EViews.5 was used at various

stages to carry out the tests.

6.4.2 The process in each case and the Hypotheses tested are summarized as

follows.

139

Table 6.4 .2

ADF unit root test

Ho : δ = 0 or ρ = 1 and H1: | ρ │ < 1 Δ Yt = α + βt + δYt-1 + ut ; δ = (ρ - 1 ) and β ≠ 0

mt t-1 i t-i t

i=1 Y = + t + + Y +Y uα β δ θΔ Δ∑

Johansen’s Co-integration test

Ho : ut is stationary : ut = Yt - α Xt.

where Xt and Yt are non stationary and are I(1) processes

Granger Causality test

Ho : Xt does not Granger Cause Yt : αj = 0 for all j

Yt = 1

1 1

n n

j t j j t j t

j j

X Y uα β− −

= =

+ +∑ ∑

H0 : Yt does not Granger Cause Xt : λj = 0 for all j

Xt = 2

1 1

n n

j t j j t j t

j j

Y Xλ δ− −

= =

u+ +∑ ∑

Where Xt and Yt represent the exchange rate and share price or

index respectively.

The underlying series are assumed to be trend stationary

140

6.4.3 The Test Process: Time Series Analysis Flowchart

Step 1 : Check for Stationarity. Daily Exchange

Rate series Share price Daily Closing series

Convert the series into Log series

Convert the series into Log series

Y

N

Check the series for stationarityADF test /PP test

ρ =1

NO UNIT ROOT Ex Rate series stationary @ 1st Difference

Step 2

NO UNIT ROOT Share Price series stationary @ 1st Difference

Check the series for stationarityADF test /PP test

ρ =1AR(P) PROCESS Take difference I(d) d=1,2,3…

AR(P) PROCESS Take difference I(d) d=1,2,3…

141

Time series analysis flow chart 6.4.3 continued

Step 2

Test for co-integration Johansen’s test @levels

142

GRANGER CAUSALITY TEST @ log levels Short run dynamics

Estimate Error Correction Model(ECM)

Results and interpretation

GRANGER CAUSALITY TEST on differences Short run dynamics

Co- integrated

6. 4 .4 PERIOD 1991 to 2006 Exchange Rate(EXR) Vs SENSEX

6.4.4.1 Unit Root Test to check on the stationarity of the series. Both series

for the period were log transformed and subjected to ADF test and Phillips-Peron

Test. The test results are summarised and presented in table 6.4.4.1 below.

Table 6.4.4.1 Unit Root Test results at levels, Series with constant and trend

Period 1991- 2006

Variable No of Observations

ADF test statistic

PP test statistic

Critical Value @ 5%

LEVEL

EXR SENSEX

3914

3914

-1.687256 -2.34936

-2.620464

-2.59759

-3.41

Source: Data Analysis :Test results appendix 6.4.1.A

6.4.4.2 Since calculated value of the test statistic t is less than its critical value

(3.41), the null hypothesis is accepted , which means for both the series EXR and

SENSEX , unit root exists and they are non- stationary at their levels.

At this stage their first differences were taken and subjected to unit root

tests and the results are given in table 6.4.4.2.

143

Table 6.4.4.2 Unit Root Test results at first difference, for series with Constant and

trend Period 1991- 2006

Variable

No of Observations

ADF test statistic

PP test statistic

Critical Value @ 5%

First Difference

EXR SENSEX

3913

3913

-10.7722

-3.41 -74.34707 -60.3252 -11.70003

Source: test results appendix 6.4.1.A

At their first difference the calculated |t| > the critical value 3.41 and

hence, the null hypothesis rejected at 5% significance level i.e., unit root does not

exist.

Accordingly, time series for Exchange Rate (EXR) and SENSEX are

stationary at their first difference and are found to be integrated of order one I (1).

6.4.4.3. Co-integration Test on EXR and SENSEX for the period 1991 to

2006.

As the next step Johansen’s Co-integration test6 was applied on the

series to examine the long run equilibrium relationship, if any, between stock prices

and exchange rates, and the test results are as given in table 6.4.4.3

6 Johansen(1995)

144

Table 6.4.4.3 . Johansen’s Co-integration Test Result (Co-integration of Exchange Rate and SENSEX for the period 1991to 2006)

Series: LN_SENSEX9106 LN_EXR9106

Sample (adjusted): 7/01/1991 6/30/2006

Trend assumption: Linear deterministic trend (restricted)

Included observations: 3897 after adjustments: Lags interval (in first differences):1to23

Unrestricted Co-integration Rank Test (Trace)

Hypothesized No. of CE(s) Eigen value

Trace

Statistic (λtrace)

Critical Value

@ 5%

Prob.**

None * 0.005536 26.83722 25.87211 0.0379

At most 1 0.001345 5.235892 12.51798 0.5629

Trace test indicates 1 co-integrating equation at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Co-integration Rank Test (Maximum Eigen value)

Hypothesized No. of CE(s) Eigenvalue Max-Eigen Statistic (λmax)

Critical Value @ 5% Prob.**

None * 0.005536 21.60133 19.38704 0.0235

At most 1 0.001345 5.235892 12.51798 0.5629

Max-eigen value test indicates 1 co-integrating equation(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

** MacKinnon-Haug-Michelis (1999) p-values

145

Johansen’s Co-integration test (continued)

Unrestricted Co-integrating Coefficients (normalized by b'*S11*b=I):

LN_SENSEX9106 LN_EXR9106 TREND(7/2/91)

-4.100521 -13.5363 0.002985

-1.504022 5.503478 0.000183

Unrestricted Adjustment Coefficients (alpha):

D(LN_SENSEX9106) 0.001276 5.26E-05

D(LN_EXR9106) 1.84E-05 -0.00013

1 Co-integrating Equation(s): Log likelihood 26704.25

Normalized co-integrating coefficients (standard error in parentheses)

LN_SENSEX9106 LN_EXR9106 TREND(7/2/91)

1.0000 3.301125 -0.000728

Std error (0.70934) (0.00012)

Adjustment

coefficients Std .Error

Test

statistic

Critical

value @5%

D(LN_SENSEX9106) - 0.0052347 (0.00114)

146

4.59123

> 1.96

Significant

-7.54E-05 (0.0002) 0.31417 D(LN_EXR9106)

Source : Data Analysis : EViews 5 Test results obtained on data series on daily exchange rate and

SENSEX (data in soft copy on CD ROM)

According to the results of the Johansen’s Co-integration test as

presented in Table 6.4.4.c above, Maximal eigen statistic (λmax) of 21. 60133 is

greater than the 5 % critical value of 19.38704 and the trace test statistic (λtrace) of

26.83722 is greater than the critical value of 25.87211. The null hypothesis of no

co integration (ie. r = 0) is rejected. The test statistic λtrace (1) = 5.235892 is less

than the 5% critical value of 12.51798 and we cannot reject traces at most one

co-integrating equation at the 0.05 level. This means there exists a single (ie. r = 1)

co- integrating relationship between the variables.

7 negative coefficient indicates the adjustment of stock price to Exchange rate equilibrium in a day

From the foregoing analysis based on high frequency data of daily

rates of exchange rate and SENSEX over the period 1991 to 2006, it could be

concluded that the two variables - Exchange Rate and SENSEX- are co-integrated

This in turn suggests long - run equilibrium relationship between the two variables.

The adjusting coefficients indicate that SENSEX adjusts to the disequilibrium

created by the exchange rate fluctuations – the speed of adjustment is indicated by

the adjustment coefficient.

6.4.4.4 The dynamics of the short run relationship between the variables was

studied by testing the causality between them. Granger Causality test was used for

the same. Causality is lag- dependent. It may be bi- directional, uni-directional and

at some lags causality may be absent. As it is important to use the optimal lag to

derive meaningful results, software support was taken. Using Eviews.5 software8,

the Final Prediction Error (FPE) criterion and Akaike’s Information Criterion

(AIC) were applied to find out the appropriate lag length. For the series – EXR

and SENSEX- under consideration the optimal lag was found to be 24 (Appendix

6.4.1 A) for which Granger Causality test gave the following results.

147

8 Eviews .5 a software for econometric analysis facilitates identification of the optimal lag using various criterion Final

Prediction Error(FPE) and Akaike’s Information Criterion (AIC): Schwarz information Criterion (SIC): Hannan-Quinn (HQ) information criterion etc. under the unrestricted VAR option for the lag structure and the lag length.

Table 6.4.4 .4 : Pair-wise Granger Causality Tests(1991-2006) Sample: 7/01/1991 to 6/30/2006 Lags: 24 No .of Observations 3897 Null Hypothesis: F-Statistic Probability LN_EXR9106 does not Granger Cause LN_SENSEX9106 2.59075 3.50E-05*** LN_SENSEX9106 does not Granger Cause LN_EXR9106 2.34028 0.00024*** *** denotes significance at 1% level Source : Data Analysis : EViews 5 Test results obtained (soft copy in CD ROM)

The F- values and the p- values in the test results above (table 6.4.4.d) suggest that

the null hypotheses of No Causality are significant at 1% and therefore can be

rejected. There is evidence of Bi- directional causality from Exchange rate to Stock

Price and from Stock price to Exchange Rate which means in the short run there is

interchangeable lead- lag relationship between the two variables.

6.4.4.5 Having established the existence of co-integration, an Error Correction

Model was developed for the co-integrating series and for brevity the details of the

vector error correction estimates are transferred to appendix(6.4.1.C).

6.4.4.6 Based on the empirical results obtained in the foregoing test results of the

high frequency daily data on exchange rate and SENSEX, for the fifteen year

period July 1991to June 2006, the nature of the inter-relationship between them

can be concluded as follows . The non stationary property of both data sets at

their levels and the presence of unit root in them are indicative of an evolutionary

component in both EXR and SENSEX. The Exchange rate and Share prices do

follow a dynamic empirical relationship in the short term. As they are co-integrated

148

they may share a common trend suggesting a long run equilibrium relationship.

Thus the existence of long term equilibrium relationship within a fully dynamic

framework suggests that a change in Exchange rate can bring about permanent

shift in the equilibrium level of SENSEX.

6.4. 5 The Interaction Of (EXR And SENSEX) In Various Time

Buckets .

Phase 1 (July1991 - July 1995),

Phase 2(August1995 - December2000) and

Phase 3 (January 2001 – June 2006)

Time series analysis was carried out for the datasets in the various time

buckets identified in line with the structural breaks in the earlier part (6.1.5)of this

chapter, to find out the variation if any , in the interrelationship between exchange

rate and share prices during three different phases. Similar to the test carried out for

the fifteen years period (1991 to 2006), ADF test for unit root, Johansen’s Co -

integration test and Granger causality test were carried out. In each case, the

optimal lag was separately found out using AIC and FPE criteria.

Having illustrated the full process of testing, in the foregoing sections 6.4.4.

1 to 4 , for the data set EXR Vs SENSEX 1991- 2006, only relevant extracts of

the test results are incorporated in the chapter, for the rest of the data sets

The detailed results are incorporated in soft copy on the CD- ROM

attached.

6.4.5.1. PHASE 1: Time Bucket : July 1991 to July 1995

149

Phase 1 was the initial trial period for both markets emerging out from a state of

internal crisis and liberalizing and getting exposed to exogenous shocks.

6.4.5.1.1 Test for Stationarity : Augmented Dickey Fuller Test

Table 6.4.5.1.1 July1991 - July1995 EXR AND SENSEX

Unit Root Test results, Constant with trend.

( Level and First difference)

Variable

Number of

observations

ADF test

statistic

Probability Critical value

@ 5 %

EXR -1.9929 .6040 Level

SENSEX

1048

150

-2.3345 .4142

EXR -7.347 .0000

-3.41 First

Difference

1047

SENSEX -6.1668 .0000

From the results of the ADF test given in table 6.4.5.1.1, it can be seen

that at levels the absolute values of the test statistic obtained for both EXR and

SENSEX are less than the critical value @5% and the corresponding p values are

greater than 0.05 and hence the null hypothesis that the series have unit root is

accepted . Therefore both the series are Not stationary. The ADF test was carried

out on the first differences for the two series . The absolute t- values are greater

than the critical values and corresponding p values are less than .05 and are

statistically significant. Therefore the null hypothesis that unit roots are present cannot

be accepted and hence the two series are stationary at their first difference.

6.4.5.1. 2. Summary of the results of the Co-integration tests for the period (July

1991- July 1995) are given in table 6.4.5.1.2 below. Using FPE and AIC criteria, a

lag length of 18 was found to be appropriate for the data set for this period.

Table 6.4.5.1.2 Johansen’s Co-integration

Test9 Lag : 18

July1991- July1995

EXR AND SENSEX

Unrestricted Co-integration Rank Test Max-Eigen value test

Hypothesized No. of CE(s)

Eigen Trace (λtrace)

Critical value @ 5 % Prob.**

Max Eigen Value λmax

Critical value @ 5 % Prob.** value

None 0.01408 19.9207 25.8721 0.2299 14.8647 19.3870 0.2011

At most 1 0.00481 5.0561 12.5180 0.5883 5.0561 12.5180 0.5883

Trace test indicates no co-integration at the 0.05 level

Max-Eigen value test indicates no co integration at the 0.05 level

With reference to the results summarized in Table 6.4.5.3 above,

Maximal eigen statistic (λmax) of 14. 86468 is less than the 5 % critical value of

19.3870 t and the trace test statistic (λtrace) of 19.9207 is less than the critical value

of 25.8721 and the corresponding p - values above .05. Therefore, the null

hypothesis of no co integration (ie. r = 0) cannot be rejected. Hence the time series

of the two variables during the time bucket July 1991 –July 1995, are not co-

integrated. We cannot trace any co- movement or long-run equilibrium for the

period.

6.4.5.1.3 The Granger causality test gave the following results . In the absence of

co-integration the causality had to be tested using the first difference.

9 Only relevant portions of the test results are included.

151

Table 6.4.5.1.3 Pair wise Granger Causality Tests No of Observations 1066 Lag 18 Sample JULY 1991- JULY1995

F-Statistic

152

The F-values and their corresponding p- values suggest that the null

hypotheses of No causality from EXR to SENSEX and from SENSEX to EXR

cannot be accepted as they are significant(***) at 1% . This indicates two way

causality between the variables, implying inter-changeable lead- lag relationship

between EXR and SENSEX.

The co-integration test indicated there was no long run impact of

exchange rate on SENSEX during the period. The causality at the first difference

was tested and the results indicated two way causality at a lag of 18 days.

Barring the two major devaluations at the beginning of the period and

the aberrations between 1992 -1993, the rupee remained more or less stable for

most of the time. The foreign exchange market was brought under managed float

and the movement of the exchange rate was calibrated. On the other hand the

securities market encountered severe scams (1992), underwent major structural and

operational transformations like establishment of SEBI, institution of on line

trading, technology enabled National Stock exchange etc. through which the

market was just evolving into a new phase of enhanced participation from

Probability At level

LN_EXR9195 does not Granger Cause LN_SENSEX919195 4.38144 3.40E-09***

LN_SENSEX9195 does not Granger Cause LN_EXR9195 3.41036 2.00E-06***

At first difference D(LN_SENSEX9195) does not Granger Cause D(LN_EXR9195) 4.57056 9.E-10***

D(LN_EXR9195) does not Granger Cause D(LN_SENSEX9195) 3.13622 1.10E-.05***

Source : Data analysis Granger Causality test results from obtained using Eviews. 5

test results (CD ROM)

institutional investors, both foreign and domestic. Possibly all these happenings in

both markets might have formed the basis of the short term dynamics of the two

variables which is depicted in the bi-directional causality test results. The markets

were still evolving into reasonable size and shape with impending reforms in terms

of structure, regulatory framework and operational modalities, that it was too early

to be settled down to an equilibrium position.

6.4.5.2. PHASE 2: Time Bucket : August1995 - December 2000 Phase two was the most volatile phase of the study period for both markets. The

IT sector boom which boosted the markets by arousing the interest of foreign and

institutional investors were offset by the Asian currency crisis. The spillover effect

of the crisis, the political disturbances and reforms accelerating the transformation

into a liberalized market, made this a trying time for the emergent Indian financial

markets.

As a first step for analysis, the lag length was identified using FPE and

AIC criteria. A lag length of 2 was found to be appropriate for the data set for this

period.

6.4.5.2.1 Test for Stationarity : Augmented Dickey Fuller Test

153

Table 6.4.5.2.1 summarizes the results of the ADF unit root test.

Table 6.4.5 .2.1 August 1995 - December 2000

EXR AND SENSEX

Unit Root Test results, Constant with trend.

Period 1995- 2000

Variable

No of Observations

ADF test statistic

Probability Critical Value @ 5%

EXR -2.57426 0.2923 Level

SENSEX

1305

-2.50203 0.3271

EXR

154

For both the variables the absolute values of the ADF test statistic

(2.257426 and 2.50203) are below the critical value of 3.41 and, therefore, we

cannot reject the null hypothesis that there is unit root. Hence both the series are

non-stationary. At their first difference ADF test statistics are greater than the

critical values and hence the null hypotheses are rejected at 5% significant level.

Therefore, the two series do not have unit roots at their first difference and hence

they are stationary at their first difference.

These findings on the non- stationarity property of the series are similar to that of

its master series and of the series for the previous phase (July1991-July 1995).

6.4.5.2.2 The Johansen’s Co- integration test was carried out to check for any

long term co- movement of the two series during the period and the results are

summarized below.

-22.4533 .0000 First

Difference

1304 SENSEX -21.2249 .0000

-3.41

Table 6.4.5.1.2 Johansen’s Co-integration

Test Lag : 18

August 1995 – December 2000

EXR AND SENSEX

Unrestricted Co-

integration Rank Test Max-Eigen value test

Hypothesized No. of CE(s)

Eigen Trace (λtrace)

Critical value @ 5 % Prob.**

Max Eigen Value λmax

Critical value @ 5 % Prob.** value

None 0.00951 18.3386 25.8721 0.3216 13.5070 19.3870 0.2889

At most 1 0.00341 4.83152 12.5180 0.6207 4.83152 12.5180 0.6207

Trace test indicates no co-integration at the 0.05 level

Max-Eigen value test indicates no co integration at the 0.05 level

Comparing the values in the above table 6.4.5.2.2, the Maximal eigen statistic

(λmax) of 13. 5070 and the trace test statistic (λtrace) of 18.3386 are less than their

critical values at 5% and the corresponding p- values are above .05 which implies No

co-integration between the two variables during the period.

6.4.5.2.3 The causality tests were also conducted to understand the short term

dynamics of the two variables during the period. In the absence of any co-

integration between the two the causality at the first difference was carried out. The

results are summarized below.

155

Table 6.4.5.2.3 Pair-wise Granger Causality Tests

Observations 1414 Sample August 1995- December2000 ( EXR and SENSEX)

Null Hypothesis: F-Statistic ProbabilityLag 2 at level

LN_SENSEX 95- 00 does not Granger Cause LN_ EXR 95- 00 0.99312 0.37068 LN_EXR 95- 00 does not Granger Cause LN_SENSEX 95–00 0.63949 0.52771 At first difference

D(LN_SENSEX 95- 00) does not Granger Cause D(LN_ EXR 95- 00) 1.15405 0.31565 D(LN_EXR 95- 00) does not Granger Cause

156

D(LN_ SENSEX 95–00) 1.42619 0.24057

The F- statistic obtained for both EXR and SENSEX are not

significant as testified by their p values which are above .05, which means there is

no sufficient evidence to reject the null hypothesis of no causality. Therefore the

above test results do not support any short – run relationship between the two

variables.

The findings as above, which implies no statistical evidence of any inter-

relationship between the EXR and SENSEX, did not match with the general belief

which was created during the crisis period when the two variables appeared to

tumble down together creating crisis in both currency and stock market. The fact

that the rupee fell in tandem with the stock market, posed the broader question of

the linkage between them.

The researcher had undertaken a survey among twenty investment and

fund managers of asset management companies based in Kochi and Mumbai.

Majority of them believed that there was a positive correlation between the

weakening of the rupee and the falling stock prices, but a good number of them

felt it was temporary. It was felt that as the period under consideration was not a

normal period and the findings did not match common expectations, it was subject

to further analysis with shorter time breaks at a later stage of the study and

described in section 6.4.9.

6.4.5.3. PHASE 3: Time Bucket : January 2001 - June 2006

Phase 3 covered the most recent period mostly representing a period of

normalcy and growth and after the dusty crisis period. The initial two years 2001

and 2002 were faced with some disturbances like the September 11 terrorist

attack, followed by global recession. Since 2003 normalcy was restored and

economic recovery was registered globally. For Indian financial markets it was a

period of further liberalization and maturing.

The ADF test results are summarized and presented in the following table.

January 2001 – June 2006 ( EXR and SENSEX) Table 6.4.5.3.1 Unit Root Test results Constant with trend

Variable Number of observations

ADF test statistic

Probability Critical value @ 5 %

EXR -1.57955 0.8008 Level SENSEX

1430 -2.323938 0.4201

EXR

157

The ADF test statistic values, obtained in the test result were not

significant for the series at their level, suggesting existence of unit root. But

-13.12125 0.0000

-3.41 First Difference 1429 SENSEX -14.16221 0.0000

6.4.5.3.1 Test for Stationarity: ADF Unit Root Test

at first difference |t| > the critical value 3.41 and hence , the null hypothesis is

rejected at 5% significance level i.e., unit root does not exist. However the ‘t’

values at their first difference are greater than the critical value at 5% and we reject

the null hypothesis that they have unit roots. Therefore, time series for Exchange

Rate (EXR) and SENSEX are stationary at their first difference and are integrated

of order one I (1).

6.4.5.3.2 The two series were subject to Johansen’s co integration test to find

out if there is any co movement of the two series leading to long term equilibrium

the results are summarized as follows.

The Maximal Eigen value (λmax)= 9.422437and the Trace Statistic

(λtrace) = 13.2128 obtained in the tests were less than their corresponding critical

values at 5% and hence the null hypothesis of No co integrating equations cannot be

rejected . This implies there is no evidence of long – term equilibrium and they do

not share any common trend. A lag length of 6 was found appropriate by FPE and

AIC criteria and it was used for the various tests for the data in phase 3.

Table 6.4.5.3.2 Johansen’s Co-integration Test EXR and SENSEX 2001 - 2006 Unrestricted Co-integration Rank Test Max-Eigen value test

Hypothesized No. of CE(s)

Eigen value

Trace (λmax)

Critical Value@ 5% Prob.**

Max Eigen Value (λmax)

Critical Value @ 5% Prob.**

None 0.006545 13.2128 25.8721 0.7215 9.422437 19.38704 0.6804

At most 1 0.002638 3.79036 12.5180 0.7722 3.790358 12.51798 0.7722

Result Trace test indicates NO co-integration at the 0.05 level

Max-eigen value test indicates No co integration at the 0.05 level

158

6.4.5.3.3 Granger Causality Test :

In the absence of any co-integration between the two, the causality at the first

difference was carried out. The results for both level and a first difference are

summarized below.

Table 6.4.5.3.3 Granger pair wise causality test Obs 1435 Sample 2001 - 2006 EXR and SENSEX

Null Hypothesis: F-Statistic ProbabilityLag 6 @ level LN _SENSEX2106does not Granger LN_ EXR 2106 4.28014 0.00028***

LN_EXR 2106 does not Granger cause LN _SENSEX 2106 1.5376 0.16218 Lag 6 @ first difference

0.00015***D(LN_SENSEX2106)does not Granger Cause D(LN_ EXR 2106) 4.51284

159

Chart 6.4.5 aRupee to a US Dollar

38

40

42

44

46

48

50

1-Jan-01 16-M ay-02 12-M ay-05 30-Jun-06

D(LN_EXR 2106) does not Granger Cause D(LN _SENSEX 2106) 1.39997 0.21111

The F- values obtained are significant(***) at 1% for the null hypothesis of

no causality of SENSEX on EXR and is testified by the corresponding p- values as

per table 6.4.5.3.3. This implies there is short run relationship. The causality runs

from stock prices to exchange rate.

This phase was a period of rigorous transformation for Indian financial

markets and the markets went through a full

cycle of change. Although partly fostered by

RBI interventions, the rupee was subject to

the market realities and more integrated to

global happenings. There was hardly any

momentum in the equity market in the first half of the period up to 2003, amidst

the global slowdown. The rupee witnessed its worst rate of above Rs.49 to a US

Dollar and completed a full cycle of weakening and strengthening during the

period 2001-2006.

CHART 6.4.5.bSENSEX

3972.002600.123114.05

5358.35

12612.38

10609.25

1-Jan-01 1-Sep-01 23-May-02

12-May-04

10-May-06

30-Jun-06

The equity market also appeared to be aligned to global trends. After the

long lull of global slow down there was all round recovery since 2003 in the market

and the markets moved to a different platform scaling new heights in tandem with

the strong fundamentals of the economy. Many initiatives by the government,

regulators RBI and SEBI helped in taming the markets to a more investor friendly

and matured stage. This aroused the interest and increased the confidence of the

investors, foreign and domestic,

institutional and individualistic. The

market gained momentum discovering

new strengths reflective of strong

fundamentals which attracted increased

inflow of capital recognizing India as a

‘sweet spot for investment’ for both Portfolio Investors and Foreign Direct

investors. The causality running from stock market prices to foreign exchange rate

could be partly explained by the dominance of foreign capital flows than trade

flows.

160

Having identified the significant shift in the parameters of the financial

matrix, a more intensive search was done for reconfirming the nature of their

behaviour using indices of broader coverage viz. BSE 100, BSE 200, BSE 500,

with respect to the changes in exchange rate. All these BSE indices reflect the

growth in market value of constituent stocks over the base period in rupee terms

and are broad-based indices, which can also reflect the movement of stock prices

on a national scale.

Applying the FPE and AIC criteria the optimal lag in each dataset was

determined and was found to be 6. The EXR series considered is the same and

hence stationarity of EXR would be the same (non- stationary at level and

stationary at the first difference) for the various sets of the data. The same test

procedures as in the case of SENSEX 30 were used and the results are summarized

and presented in forthcoming sections from 6.4.6.1 to 6.4.7.3.3. Only relevant

extracts of the test results are incorporated in the printed report, while the

exhaustive details are omitted. The details are however included in the soft copy

along with the relevant data. For brevity sake the analytical comments are

consolidated at the end of the each set of series.

161

6.4. 6. 1 BSE 100

Table 6.4.6.1.1.Unit Root Test results level and First difference Constant with trend

Period 2001 - 2006

Variable

Number of observations

ADF test statistic

Probability Critical value @ 5 %

EXR -1.4986 0.1342 Level

BSE 100

1430 -2.9941

162

Table 6.4.6.1.2 Johansen’s Co-integration Test EXR and BSE100 2001 - 2006

Unrestricted Co-integration Rank test Max-Eigen value test

Hypothesized No. of CE(s)

Eigen Value

(λtrace) Trace

0.05 Critical Value Prob.**

(λmax) Max Eigen value

0.05 Critical Value Prob.**

None 0.00887 15.4515 25.8721 0.5372 12.7243 19.3870 0.3508

At most 1 0.00191 2.72721 12.5180 0.9073 2.72721 12.5180 0.9073

The values obtained for Trace (λtrace) = 15.4515 and (λmax) = 12.7243 were

both less than their corresponding critical values and therefore , indicate No Co-

integration at the 0.05 level. This implies that there was No long–term equilibrium

relationship between EXR and SENSEX during the period (2001-2006).

0.1341

EXR -13.4508 0.0000 -3.41 First

Difference BSE 100

1429 -14.1614 0.0000

Comparing the ADF test statistic obtained as above with the critical

values and the corresponding p values the hypothesis that BSE 100 series

has unit root cannot be rejected at level and can be rejected at their first

difference. Hence the series are stationary at their first difference.

Table 6.4.6.1.3 Pair wise Granger Causality Tests EXR and BSE 100 Obs 1431 Lag 6 Sample 2001 - 2006

Null Hypothesis: F-Statistic Probability At level LN_EXR does not Granger Cause LN_BSE 100 0.42459 0.86296

LN_BSE 100 does not Granger Cause LN_EXR 2.70728 0.01285**

At first difference

D(LN_EXR) does not Granger Cause D(LN_BSE100) 0.26325 0.95395

D(LN_BSE100) does not Granger Cause D(LN_EXR) 2.63058 0.01535**

The F- statistic for the no causality hypothesis for EXR over SENSEX

and the corresponding p values suggest that there is no causality of EXR over BSE

100 meanwhile the null hypothesis for BSE100 causes SENSEX is Significant(**) at

5%. Hence there is uni-directional causality running from Stock prices to EXR.

6.4.6.2 BSE 200

Table 6.4.6.2.1. Unit Root Test results Constant with trend

Period 2001 - 2006

Variable

Number of observations

ADF test statistic

Probability Critical value @ 5 %

EXR -1.498643

163

0.1342 Level BSE 200

1430

-3.11695 0.1026 EXR -14.1541 0.0000

-3.41 First Difference

1429

-13.45082 0.0000 BSE 200

Comparing the ADF test statistic obtained as above with the critical

values and the corresponding p values the hypothesis that BSE 200 series

has unit root cannot be rejected at level and can be rejected at their first

difference. Thus the series were found to be stationary at their first difference.

Table 6.4.6.2.2

Johansen’s Co-integration Test

EXR and BSE 200 2001 - 2006

Unrestricted Cointegration Rank test Max-Eigen value test

Hypothe-sized No. of CE(s)

Eigen value

Trace (λtrace)

Critical Value @5%

Prob**

Max Eigen Value (λmax)

Critical Value @5% Prob.**

None 0.0087 14.6942 25.8721 0.600 12.5199 19.3870 0.3683

At most 1 0.0015 2.17430 12.5180 0.957 2.17430 12.5180 0.9570

The values obtained as per table 6.4.6.2.2, for Trace (λtrace) and (λmax)

are both less than the corresponding critical values and therefore indicate No

Co-integration at the 0.05 level, This implies that there is No long –term

equilibrium relationship between Exchange rate and stock prices represented by

BSE200 for the period(2001 – 2006)

Table 6.4.6.2.3 Pair wise Granger Causality Tests BSE 200 AND EXR

Obs 1431 Lag 6 Sample 2001 - 2006

Null Hypothesis: F-Statistic ProbabilityAt level

0.30889 0.93256 LN_EXR does not Granger Cause LN_BSE200 LN_BSE200 does not Granger Cause LN_EXR 2.47081 0.02213**

At first difference D(LN_EXR) does not Granger Cause D(LN_BSE200) 0.25368 0.95793

Considering the p - values of the F statistics as per table 6.4.6.2.3, the null

hypothesis of No causality of BSE200 on EXR is significant at 5%(**) and hence

cannot be accepted .Therefore BSE200 causes EXR. The F- statistic for EXR and

the corresponding p - values suggest that there is no causality of EXR over stock

D(LN_BSE200) does not Granger Cause D(LN_EXR ) 2.43283 0.02413**

164

Table 6.4.6.3. EXR and BSE 500

Table 6.4.6.3.1 Unit Root Test results Level & First difference , Constant with trend

Period 2001 - 2006

Variable

Number of observations

ADF test statistic

Probability Critical value @ 5 %

EXR -1.498643

0.1342

Level

BSE 500

1430 -2.98273 0.1374 EXR

-13.45082 0.0000 First Difference

165

prices represented by BSE200. BSE 500

1427

-14.0465 0.0000

Comparing the ADF test statistic obtained as above with the critical

values and the corresponding p values the hypothesis that BSE 500 series has

unit root cannot be rejected at level can be rejected at their first difference as it is

significant at 5%. Hence the series are stationary at their first difference

-3.41

Table 6.4.6.3.2 Johansen’s Co-integration Test EXR and BSE500 2001 - 2006

Unrestricted Cointegration Rank test Max-Eigen value test

Hypothesized No. of CE(s)

Eigen value

Trace (λtrace)

Critical Value@

5%

Critical Value@

5% Prob.**

Max Eigen value (λmax) Prob.**

None 0.0085 14.5061 25.8721 0.6157

11.9883 19.3870 0.4163

At most 1 0.0018 2.51776 12.5180 0.9282 2.51776 12.5180 0.9282

The values obtained for (λtrace) = 14.5061 and (λmax) = 11.9883 are both

less than the corresponding critical values and therefore indicate No Co

integration at the 0.05 level, This implies that there is No long – term equilibrium

relationship between EXR and BSE 500 during the period.

Table 6.4.6.3.3. Pair wise Granger Causality Tests BSE 500 AND EXR Obs 1406 Lag 6 Sample 2001 - 2006

Null Hypothesis: F-Statistic Probability At level LN_EXR does not Granger Cause LN_BSE 500 0.37763 0.89356

0.02966**LN_BSE 500 does not Granger Cause LN_EXR 2.34134

At first difference

D(LN_EXR) does not Granger Cause D(LN_BSE500) 0.27475 0.94892

2.25723 0.03579**D(LN_BSE500) does not Granger Cause D(LN_EXR) The F values for the null hypothesis BSE500 does not causes EXR is significant

at 5%( **)and cannot be accepted which implies short term causal effect of

BSE500 on EXR.

The BSE 100, BSE200 and 500 series are all non-stationary at levels and stationary

at the first difference. The Maximal Eigen value (λmax )and the Trace Statistic (λtrace)

are below the corresponding critical values and hence cannot reject the null

hypotheses of no co integrating equations do not bear any long term relationship with

exchange rate for the period . The F values of the causality test and the

corresponding p-values of three series are significant at 5 % suggesting only uni-

directional causality running from Sock Prices to Exchange Rate. Hence the test results

support short term influence of BSE indices representing stock prices on the

exchange rates where as there is no such evidence for exchange rate causing

166

impact on stock prices during the period (2001-2006).

6.4.7 Dollex Series reflect, in one value, the changes in both the stock prices

and the foreign exchange variation by expressing the current and base market

values in dollar terms. The scope for dollar-linked index emerged from the

background of Indian equity markets increasingly getting integrated with global

capital markets. In the context of Indian equities getting listed on foreign stock

exchanges and with the increased participation of FIIs in the Indian equity market

dollar benchmarked indices, which would assess the market movements in terms of

international benchmarks became very relevant. This dollar-linked index is useful to

overseas investors, as it helps them measure their 'real returns' after providing for

exchange rate fluctuations.

The basis of computation of the various indices and the specific details on base

period, coverage etc are provided in the glossary of terms , for ready reference.

DOLLEX 30, DOLLEX 100 and DOLLEX 200 for the period 2001 to 2006 were

taken, tested for time series properties and checked for co-integration and causality

and the results are summarized in the tables 6.4.7.1 to 6.4.7.3.3.

167

168

Table 6.4.7.1.2 Johansen’s Co-integration Test EXR and DOLLEX 30 2001 - 2006

Unrestricted Cointegration Rank test Max-Eigen value test

Hypothesized No. of CE(s)

Eigen value

Trace (λtrace)

Critical Value@ 5% Prob.**

Max Eigen value (λmax)

Critical Value@ 5% Prob.**

None 0.00759

7 14.487

6 25.87211 0.6172 10.8982 19.38704 0.5239

At most 1 0.00250

9 3.5894 12.51798 0.8003 3.5894 12.51798 0.8003 The values of (λtrace) = 14.4876 and (λmax) = 10.8982 are less than

their critical values and the null hypothesis Not rejected. Trace test and Max-

eigen value test indicate No Co-integration of EXR and DOLLEX 30 at the

0.05 significance level.

6.4.7. 1 DOLLEX 30

Table 6.4.7.1.1. Unit Root Test results Level & First difference , Constant with trend

Period 2001 - 2006

Variable

Number of observations

ADF test statistic

Probability Critical value @ 5 %

EXR

-1.498643

0.1342

1430 Level

DOLLEX 30 -2.64989 0.2581

-3.41

EXR -13.45082 0.0000 First Difference

DOLLEX 30

1427

-14.1231 0.0000 Comparing the ADF test statistic obtained as above with

the critical values& the corresponding p values the hypothesis that

DOLLEX30 series has unit root cannot be rejected at level can be rejected at

their first difference as it is significant at 5%. Hence the series are stationary

at their first difference.

Table 6.4.7.1.3 Pair wise Granger Causality Tests Obs 1406 Lag 6 Sample 2001 - 2006 EXR and DOLLEX 30

Null Hypothesis: F-Statistic Probability At level LN_EXR2106 does not Granger Cause LN_ DOLLEX 30 0.46189 0.83681 LN_ DOLLEX 30 does not Granger Cause LN_EXR2106 4.41106 0.0002***

At first difference

D(LN_EXR) does not Granger Cause D(LN_DOLLEX 30) 0.30962 0.93219

D(LN_DOLLEX 30) does not Granger Cause D(LN_EXR) 4.21076 0.00033***

Considering the p- values of the F statistics as per table 6.4.7.1.3, the

null hypothesis of No causality of DOLLEX30 on EXR is significant(***) @ 1%

and hence rejected. This implies short term impact of DOLLEX on EXR.

However, there is no causality of EXR over DOLLEX30.

6.4.7.2 DOLLEX 100 2001-2006

Table 6.4.7.2.1. Unit Root Test results Level and First difference , Constant with trend

Period 2001 - 2006

Variable

Number of observations

ADF test statistic

Probability Critical value @ 5 %

EXR

-1.498643

169

0.1342 Level

DOLLEX 100

1430

-3.022072 0.1264

EXR -13.45082 0.0000

-3.41 First Difference

DOLLEX 100

1429

-14.07485 0.0000

The ADF test statistic is significant at 5 % for first difference and

therefore, the Null Hypothesis is rejected. i.e UNIT ROOT does NOT Exist

and hence the series is stationary at first difference.

Table 6.4.7.2.2 Johansen’s Co-integration Test EXR and DOLLEX 100 2001 - 2006 Unrestricted Co integration Rank Test Max-Eigen value test

0.05 Hypothesized No. of CE(s)

Eigen value

Trace (λtrace)

0.05

170

Critical Value Prob.**

Max Eigen value Critical

Value (λmax) Prob.**

None 0.008834 15.48264 25.87211 0.5346 12.6799 19.3870 0.3545

At most 1 0.001959 2.802735 12.51798 0.8991 2.80274 12.5180 0.8991

The values of (λtrace) = 15.48264 and (λmax) = 12.6799 are less than their critical

values Therefore, the null hypothesis can not be not rejected. Thus the Trace test and

Max-eigen value test indicate No Co-integration between EXR and DOLLEX 100 at the

0.05 level of significance.

Table 6.4.7.2.3 Pair wise Granger Causality Tests Obs 1431 Lag 6 Sample 2001 - 2006 EXR and DOLLEX 100

Null Hypothesis: F-Statistic Probability At level LN_EXR2106 does not Granger Cause LN_ DOLLEX 100 0.56299 0.76003

LN_ DOLLEX 100 does not Granger Cause LN_EXR2106 3.60988 0.00148***

At first difference

D(LN_EXR) does not Granger Cause D(LN_DOLLEX 100) 0.55982 0.76254

D(LN_DOLLEX 100) does not Granger Cause D(LN_EXR) 3.72761 0.0011***

The F- statistic is significant(***) @ 1% The null hypotheses of No causality for

DOLLEX 100 over EXR is rejected. This implies short term impact , where Causality runs

from DOLLEX100 to EXR . However , there is no causality of EXR over stock prices

represented by DOLLEX100

6.4.7.3 DOLLEX 200 2001-2006 Table 6.4.7.3.1.Unit Root Test results First difference , Constant with trend Period 2001 - 2006

Variable

Number of observations

ADF test statistic

Probability Critical value @ 5 %

EXR

171

Table 6.4.7.3.2 Johansen’s Co-integration Test

EXR and DOLLEX 200 2001 - 2006

Unrestricted Cointegration Rank Test Max-Eigen value test

Hypothesized No. of CE(s)

Eigen value

Trace (λtrace)

0.05 Critical Value Prob.**

Max Eigen value (λmax)

0.05 Critical Value Prob.**

None 0.008715 14.69145 25.87211 0.6002 12.50902 19.38704 0.3693

At most 1 0.001526 2.182427 12.51798 0.9564 2.182427 12.51798 0.9564 The values of (λtrace) and (λmax) are less than their critical values .at

5%. Null hypothesis cannot be rejected. The Trace test and Max-eigen value tests,

therefore indicate No Co-integration between EXR and DOLLEX200 at the 0.05 level.

-1.498643

0.1342 Level

1430

DOLLEX 200 -3.094838 0.1079 EXR -13.45082 0.0000 First

Difference DOLLEX 200

1427 -14.20332 0.0000

-3.41

Comparing the ADF test statistic obtained as above with the critical values

and the corresponding p values the hypothesis that DOLLEX200 series has

unit root cannot be rejected at level can be rejected at their first difference as it is

significant at 5%. Hence the series are stationary at their first difference.

Table 6.4.7.3.3 Pair wise Granger Causality Tests

Obs 1431 lag 6 EXR and DOLLEX 200 Sample 2001 - 2006

Null Hypothesis: F-Statistic Probability

At level LN_EXR2106 does not Granger Cause LN_ DOLLEX 200 0.30224 0.93592 LN_ DOLLEX 200 does not Granger Cause LN_EXR2106 3.95686 0.00062***

At first difference

D(LN_EXR) does not Granger Cause D(LN_DOLLEX 200) 0.26928 0.95134

D(LN_DOLLEX 200) does not Granger Cause D(LN_EXR) 3.88796 0.00074***

The F- statistic is significant (***) @ 1% for the null hypotheses of No causality of

DOLLEX 200 over EXR and hence rejected. This implies short term impact,

where Causality runs from DOLLEX200 to EXR . However, there is no causality

of EXR over stock prices represented by DOLLEX200.

6.4.8. Summary of the findings for the various BSE and DOLLEX Series

The findings from the foregoing analyses are summarized in table 6.4.8.

The extensive search using various indices with broader coverage to represent the

stock prices, reveals some common truth about the nature of impact between

exchange rate and share prices in the Indian capital market for the period 2001 to

2006.

172

Table 6.4.8

BSE SERIES AND DOLLEX SERIES Causality and Co -integration Test Results Summary

(Period 2001 - 2006 Number of observations 1435)

INDEX LAG CAUSALITY ( at Level & first Difference) COINTEGRATED

1 BSE 30 SENSEX 6

SENSEX EXR No

2 BSE100 6

BSE100 EXR No

3 BSE200 6

BSE200 EXR No

4

173

BSE500 6 BSE500 EXR No

5 DOLLEX 30 EXR DOLLEX 30 6 No

6

DOLLEX 100 EXR DOLLEX 100 6 No

7 DOLLEX 200 6 DOLLEX 200 EXR No

There is some uniform pattern of impact for all the broad based indices

during the period 2001- 2006. None of them could establish a long

term equilibrium relationship with the exchange rate during the period. However

the causality test results are consistent for the various indices with stock prices

found to be Granger causing exchange rates indicating that fluctuations in the stock

market lead the changes in the foreign exchange markets.

This also supports the portfolio balance approach to exchange rate determination. This

may be particularly true in the context of an economy which is increasingly getting

opened and integrated to the global market.

6.4.9 Unique Behavioral Patterns In Special Time Zones.

Specific patterns of rupee behaviour in terms of consistent depreciation,

appreciation and high volatility along with the corresponding time buckets- short,

medium and long term - were identified. The behaviour of share prices as indicated

by the daily closing SENSEX values were matched with the daily Exchange Rate

and their relationship studied to check whether there is a co-integrating and/or

causal relationship between the variables. The following Special Time Zones were

identified.

• July 1991 – March 1992. Devaluation of the Rupee marking the first step of

economic reformation.

• March 1992 – February 1993: Dual exchange rate (LERMS) regime.

• March 1993 – July 1994: Introduction of market determined exchange rate.

The rupee remained very docile as the controls were slowly released and the

exchange rate management shifted from a ‘controlled regime’ to a ‘managed

float’

• August 1994 – July 1995 India adopted the current account convertibility in

August 1994. Exchange Rate more or less stable.

• August 1995 to June 1996. Period of high volatility of the rupee.

• July 1996 and July/August 1997, the rupee remained very stable (at around

Rs.35 to the dollar) with RBI intervention .

• August 1997 to June 1998 - the days of wild gyration due to the Asian

Currency Crisis. Rupee depreciating from Rs 35.7 to 42.5 against the US

Dollar:

174

• July 1998 to December 2001. Post crisis period politically disturbed India, the

Nuclear test, September 11 attack and the perturbed Dollar (sub periods also

considered)

• 2001 –2002 the long lull period : US Fed-rate hardens and the ensuing capital

flight

• 2003 – 2004 the recovery period

• May 2004 – June 06 Stable growth markets: SENSEX Scaling new heights.

• Shorter sub period aligned to the ascends and descends of the Rupee

• In the Long run 15 years , 10 years and 5 years

• In the medium term (>1 to < 5 years)

• In the short term (less than one year )

Four dimensional analyses was carried out on the different pairs of data.

Volatility determined Standard Deviation and coefficient of variation

Relative volatility expressed as a multiple comparing the coefficient of variation

of the two variable in the same period

Co integration test using Johansen’s Co integration test to identify any long

term relationship leading to equilibrium

Causality of the variables one on the other for evidence of any lead- lag

relationship using Granger causality test

The results of the various tests are compiled in a summarised form as follows.

• Table 6.4.9.1 for period (1991- 2000)

• Table 6.4.9.2 for period (2001 -2006)

175

TABLE 6.4.9.1 EXR and SENSEX 1991 -2000 SPECIFIC BEHAVIORAL PATTERNS IN SPECIAL TIME ZONES

Summary statistics

TIME ZONE

Days EXR / SENSEX Std dev.

Coefft of variation %

Relative variation (Times)

Co-integrated or NOT

Results from Granger Causality Tests

EXR 0.0011 1.41 1

01/07/1991

to 01/03/1992

175

SENSEX 492.15 14.41

10.2 Yes Yes Bi -

directional

EXR 0.0011 3.2 2

01/03/1992

to 26/02/1993

260

SENSEX 492.15 16.1

5.0 Yes Yes Bi -

directional

EXR 0.0001 0.3 3

01/03/1993 to

31/07/1994 369

SENSEX 744.56 23.9 94.0 Yes Yes

Bi - directional

EXR 0.0001 0.3 4

01/08/1994 to

31/07/1995 261

SENSEX 476.10 12.6 43.2 No

No evidence of

causality

EXR 0.0011 3.9 5

01/08/1995 to

31/07/1996 262

SENSEX 281.31 8.2

2.1 No No

evidence of causality

EXR 0.0001 0.2 6

01/08/1996 to

31/07/1997 261

SENSEX 382.05 10.8 46.1 No

YES EXR

causes SENSEX

EXR 0.0013 5.0 7

01/08/1997 to

30/06/1998 238

SENSEX 325.07 8.6 1.73 No

YES Sensex causes EXR

EXR 0.0001 0.55 8 01/08/1998

to 30/06/1999

261 SENSEX 390.49 11.8

21.30 No No

evidence of causality

EXR 0.0007 2.9 9 01/7/1998

to 31/12/2000

653 SENSEX 794.10 19.3

6.60 No Yes Bi -

directional

EXR 0.0002 0.8 10 01/7/1999

to 30/6/2000

262 SENSEX 412.87 8.5

10.2 No No

evidence of causality

EXR 0.0046

6 16.2 11

01/07/1991

to 31/12/2000

1.5 No Yes Bi -

directional

2480

SENSEX 822.70 23.6

EXR 0.0051 19.4 12

7/1/1991 to

30/6/2006 3915

SENSEX 1782.6

8 43.1 2.2 Yes

Yes Bi -

directional *Based on Results from Johansen’s Co-integration and Granger causality test details on file 6.4. CD Rom

176

TABLE 6.4.9.2 2001 -2006 SPECIFIC BEHAVIORAL PATTERNS IN SPECIAL TIME ZONES

Summary statistics

TIME ZONE

Days EXR / SENSEX Std dev.

Coefft of variation

Relative variation

Co-integrated or NOT

Results from Granger Causality Tests

EXR 0.0008 3.6 1

01/01/2001

to 30/06/2006

1435

SENSEX 2355.49 44.9

12.3 No Yes

Bi - directional

EXR 0.0004 1.8 2

01/01/2001

to 15/05/2002

358 SENSEX 371.37 10.7

6.1 YES Yes

Bi - directional

EXR 0.0003 1.6 3

21/1/2001 to

07/4/2003 638

SENSEX 311.02 9.4

5.7 No Yes Bi -

directional

EXR 0.0006 2.9 4 16/05/2002

to 31/03/2004

491 SENSEX 1013.17 26.1

9.1 No No

EXR 0.0002 0.9 5

28/11/2003 to

01/04/2004

90 SENSEX 272.48 4.8

5.3 No No

EXR 0.0004 1.9 6

01/04/2004 to

30/07/2004 87

SENSEX 425.67 8.2

4.3 No

YES SENSEX causes EXR

EXR 0.0004 2.0 7

01/04/2004 to

31/12/2004 197

SENSEX 511.29 9.3

4.7 No

YES SENSEX causes EXR

EXR 0.0005 2.3 8 02/08/2004

to 03/02/2005

134 SENSEX 474.49 8.1

3.5 No No

EXR 0.0005 2.3 9

02/08/2004 to

05/12/2005 351 SENSEX 1011.71 14.9

6.6 No No

EXR 0.0001 0.4 10 17/12/2004

to 30/09/2005

206 SENSEX 651.58 9.4

26.2 No No

EXR 0.0003 1.6 11

06/12/2005 to

31/01/2006 41

SENSEX 264.09 2.8

1.8 No No

EXR 0.0004 1.9 12

01/02/2005 to

30/06/2006 369

SENSEX 1767.08 20.6

10.6 No No

EXR 0.0005 2.1 13 1/07/2003

to 30/06/2006

784

177

SENSEX 2124.97 31.1

14.6 No

YES SENSEX causes EXR

EXR 0.0003 1.5 01/02/2006

to 30/06/2006

14 108 SENSEX 855.59 7.9

5.2 No No

*Based on Results from Johansen’s Co-integration and Granger causality test details on file 6.4. CD Rom

The following time varying nexus of dynamic relationship was

observed for different time zones during the period (ref. tables 6.4.9.1 and

6.4.9 .2).

• In the long term

15 years (1 July 1991 to 30 June 2006) Bi- directional causality.

10 years (1 July 1991 to 31 Dec 2000) Bi- directional causality .

5 years

o July1991 to July 1995 Bi- directional causality.

o July 1995 to December 2000 N0 causality.

o January 2001 to June2006 Bi- directional causality.

• In the medium term (>1 to < 5 years)

Bi-directional during1991-1992, 1993-1994, 1998-2000,

2001-2002 and 2001-2003 .

Unidirectional from EXR causing SENSEX1996-1997 and SENSEX

causing EXR 2003 -2006

• In the short term (less than one year )

Unidirectional influence of SENSEX on exchange rate during August

1997- June1998, April 2004 -July 2004 and April 2004 -Dec 2004,

which concurs with the portfolio approach according to which stock

prices lead exchange rates with a negative correlation

And no interdependence between the variables during the periods

August 1994 - July 1996, August 1998- June 1999, July 1999 - June

2000, May 2002 - March2003, Nov 2003- March 2004 , August 2004-

Feb 2005, Dec 2004 - Sept 2005, Dec 2005 - Jan 2006 and

Feb 2006 to June 2006.

178

Causality is a matter of short term dynamics. While in time zone 1996-

2000 neither causality nor co-integration was evidenced for the five year period in

toto, traces of causality was observed in shorter periods in between. The opposite

was true for the long term fifteen years (1991 o 2006) for which both co-

integration and causality tests proved with positive results supporting a dynamic

equilibrium relationship. This was not true for the sub periods suggesting that a

stable exchange rate causes and supports rising prices in the stock market and this

held true for the fairly long stretches of placidity.

6.4.9.2 There were several notable episodes which raised the volatility of the

exchange rate and mostly set contemporaneous impact on the share prices as

follows

• 1991 the double devaluation of the rupee against the dollar as the first

step of kick starting the economic reforms

The stock prices were negatively correlated with the weakening

exchange rate

Causality test revealed EXR shocks impacted the SENSEX. whereby

the Devaluations in the rupee caused the SENSEX to rise.

• 1992 LERMS - dual exchange rate raising volatility.

Both appeared to be interrelated, mutually influencing both in the

short run and long term equilibrium evidenced by the presence of

causality and co- integration.

Both Foreign exchange rate and SENSEX were highly volatile with

strong co movement, partly attributed to the securities scam.

179

• August 1994. Partial convertibility of currency instituted, rupee made

stable.

Share prices responded positively to the relatively stable rupee

Causality test revealed EXR fluctuations caused volatility in

SENSEX.

• August1997 Asian crisis.

The response to the crisis was slow but both markets were under bear

hug. Although there was no statistically significant interaction, SENSEX

seemed to lead the run down, impacting volatility on the exchange rate.

• September 2001: Terrorist attack.

This resulted in rampant weakening of the dollar, but dollar-rupee

parity remained almost stable for sometime. But the shock waves

impacted the stock market heavily. Stock market rumbled down about ten

times faster than foreign exchange market and the movement was strongly

integrated. These findings are consistent with Downburst’s (1976) findings

that “Exchange rates can temporarily overshoot in the wake of certain

disturbances.”

• The global slowdown and the recovery 2001-2002-2003

The process of global slowdown affected the liquidity of the Indian stock

market and the market went through a dull phase during 2001 to 2002. In

May 2002, with the hardening of Fed – rate, Rupee was worst hit and

there was outbound capital flight. The two markets were passive and were

independent of each other with neither co-integration nor causality.

180

Recessionary trends and economic slowdown prevailed globally.

Recovery resumed from 2003 onwards recording a smart rally. Both

markets responded favourably, supporting each other.

From 2003 onwards India transited through a steady progress.

The economic progress gave fundamental strength to both markets. A

temporary aberration in the stock market was reflected in the volatility of the

rupee as well.

• In good times and bad times

In sober market conditions, with no major shocks and where

volatility was minimal, long term relationship prevailed and there was

mutual support for each other. This was testified by the presence of co-

integration and bi-directional causality. The two variables were found to

be significantly linked in the non-crisis stable periods but unpredictable

during the crisis hit periods. In good times the two variables are closely

interrelated bearing a positive correlation; ie a strengthening rupee

supports the momentum in stock market demonstrating the fundamental

strength of the economy. The substantial surge in capital inflow of

which a major share was accounted for by FIIs triggered a bull run in

the stock market and a cyclical effect in foreign currency market.

Unlike in the past, the recent surge in capital flows into the stock

market has been more substantial and prolonged.

181

6.4.10 Impact On Various Industry Sectors (EXR Vs Sectoral indices)

The nature of the industry and the type of response they have to the fluctuations in

exchange rate need not be uniform. An analysis of the response of share prices in each

industry sector was mapped using Sectoral indices to represent the industry groups and

were regressed on daily exchange rates of Rupee against the US dollar. The summary

statistics and results of co-integration and causality test are presented in table 6.4.10

TABLE 6.4.10 EXR and SECTORAL INDICES 2001 - 2006 Summary statistics

Sector Days EXR / INDEX Std dev.

Coefft of variation %

Relative variation

Co-integrated?

Evidence of causality

EXR 0.0005 2.10 1

AUTO-MOBILE 485

BSEAUTO 1017.4 29.10 13.9 NO No evidence

of causality

EXR 0.0005 2.1 2

BANK- ING 789

BANKEX 1116.4 31.5 14.7 Yes

EXR to

BANKEX

EXR 0.0008 3.70 3

CONSUMER DURABLES

1429 BSECD 834.80 65.7

17.9 No No evidence of causality

EXR 0.0008 3.7 4 CAPITAL

GOODS 1435 BSECG 2072.2 88.9 24.3 No No evidence of causality

EXR 0.0008 3.7 5 FMCG 1435 FMCG 344.80 32.3

8.8 No No evidence

of causality

EXR 0.0008 3.7 6 HEALTHCARE

1435 BSEHC 810.80 40.1

11.0 Yes YES Bi directional

EXR 0.0008 3.7 7 INFO

TECH 1435 BSEIT 819.2 39.4

10.80 No No evidence

of causality

EXR 0.0005 2.10 8 METALS 485 BSEMETAL 1378.7 21.4

10.20 No No evidence of causality

EXR 0.0005 2.1 9 OIL 485

BSEOIL 836.12 22.3 10.60 No BSEOIL

to EXR

EXR 0.0008 3.8 10 PSU 1324

BSEPSU 1509.0 51.9 13.7 No

EXR to

BSEPSU

EXR 0.0008 3.8 11

TECHN-OLOGY 1277

BSETeck 597 44.3 11.6 Yes YES

Bi directional

EXR 0.0004 2.0 12 MIDCAP 320

182

BSEMIDCAP 762.9 17.9 8.8 No No evidence

of causality

EXR 0.0004 2.0 13 SMALL-CAP 320

BSESMALLCAP 939.5 16.8 8.4 No No evidence

of causality

Source: Data analysis: Details of descriptive statistics, test result and supporting data on CD ROM

Of the thirteen industry sectors identified, only Banking, Technology

and Healthcare sectors were found to have long run and short run relationship with

exchange rate. Share prices in OIL and Auto sectors seemed to cause short run

effect on exchange rate.

The relative volatility of each sector measured in terms of the coefficient of

variation of sector index divided by that of the exchange rate is depicted below in

Chart 6.4.10.1 below.

CHART 6.4.10.1EXR Vs INDUSTRY SECTORAL INDICES

RELATIVE CHANGE

0%10%20%30%40%50%60%70%80%90%

100%

AUTOBANK CD

CAPFMCG

HEALTH IT

METALS OIL

PSUTec

k

MIDCAP

SMLLCAP

Coe

fft.

of v

aria

tion

Sector

EXR

Source: Data analysis: Drawn based on the analysis of daily data on sector indices and exchange rate collected from RBI & BSE publications (appendix - Tables & Chart: Soft coy attached)

From chart 6.4.10.1 above it could be seen that share prices for firms in the

Capital Goods segment were mostly affected by the changes in the exchange rates,

followed by those of consumer durables, PSUs, and Teck sectors respectively. The

impact on FMCG Metals, OIL, MIDCAP and SMALLCAP sectors were among the

least.

183

6.4.10.2 The Correlation between the exchange rate and the various industry indices

is depicted in chart 6.4.10.2 below.

Chart 6.4.10.2Correlation between EXR and Sectoral Indices

2001- 2006

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1AU

TO

BAN

K CD

CAP

FMC

G

HEA

LTH IT

MET

ALS

OIL

PSU

Teck

MID

CAP

SMLL

CAP

Source: Data analysis: Prepared based on the data on daily indices and Exchange rate (2001- 2006)

Exchange-rate changes have negative effects on some industries but

positive effects on other industries. Import intensive sectors like Automobile, Oil,

Metals, small cap and mid cap companies responded negatively to the weakening

rupee. All other sectors were found to be positively correlated with the depreciating

rupee during 2002 to 2006.

6.4.11 How Individual Companies Respond To Fluctuations In Exchange

Rates.

The impact of the exchange rate fluctuations at the micro level was studied

by applying co-integration and causality tests on data on individual companies. Pairs of

data sets were formed by taking daily closing share prices of the firm and the daily

exchange rate over the period 1st January 2000 to 30 June 2006, except in case of a few

which were available for shorter periods on NSE/BSE.

184

6.4.11.1 Hundred companies were chosen based on the following criteria to understand

how individual firm’s share prices responded to the exchange rate fluctuations.

• Companies which were part of SENSEX as on 31 March 2006

• Companies which were part of NIFTY as on 31 March 2006

• And some from BSE 200 as they were either export intensive or import

intensive.

Only 99 were included in the final analysis as one of them (Reliance

Communication) was excluded for want of sufficient number of observations. For

each company daily closing prices and the corresponding days’ exchange rate of Rupee

to one US $ were collected covering the period January 2000 to June 2006(Soft copy of

data file attached).The data sets were tested for Stationarity, Causality and Co-

integration. Like in case of indices the time series of the share prices of the firms were

found to be stationary at their first difference. The results of the Co- integration and

causality tests for the selected companies are summarized as under in table 6.4.11.1

(The detailed test results are provided in the soft copy in folder named ‘Data Analysis’

in soft copy.)

185

Table 6.4.11.1 Summary of the results of Co integration and Causality Tests for

Selected Companies. Causality SL.

No Name of the company

No.of Observati

ons Lag

Co- integrated (Yes/No)

Yes/ NO

Direction of causality

1 ABB 1687 8 No Yes EXR Cause SP 2 ACC 1687 6 No Yes SP causes EXR 3 Alok Industries. 1675 6 No No 4 Arvind Mills 1687 2 Yes Yes Bi- Directional* 5 Aurobindo Pharma 1687 2 No Yes SP causes EXR 6 Aventis Pharma 1687 6 Yes Yes SP causes EXR* 7 BAJAJ AUTO 1687 6 No Yes SP causes EXR* 8 Bharat Forge 1693 6 No No 9 BHARATI Tele. 1107 8 No Yes SP causes EXR 10 Bharti Airtel Ltd. 1102 8 No Yes SP causes EXR 11 BHEL 1687 8 No Yes SP causes EXR 12 Biocon 576 3 No Yes SP causes EXR 13 BPCL 1690 2 Yes Yes SP causes EXR 14 CASTROL 1420 6 No No 15 Chennai Petroleum 154 3 No No 16 CIPLA 1693 7 No Yes Bi- Directional 17 Cummins India 1693 3 No No 18 DABUR india 1693 7 No No 19 Divi's Lab 861 2 No No 20 DRL 1693 3 No No 21 EIH 1686 6 No No 22 Essar Shipping 1687 6 No No 23 G S F C 1686 6 No No 24 GAIL 1687 6 No Yes SP causes EXR 25 Glaxo 1687 6 No Yes SP causes EXR 26 GRASIM 1687 6 No Yes SP causes EXR 27 GTL 1687 3 No Yes SP causes EXR 28 GUJARAT Ambuja 1693 2 No No 29 H P C L 1693 3 No No 30 1686 6 No HCL Infosystems No 31 1686 8 No No HCL TECh 32 HDFC 1685 11 No Yes SP causes EXR 33 HDFC BANK 1685 10 No Yes Bi- Directional

186

Table 6.4.11.1 continued..

Causality

SL. No Name of the company No.of

Observations

Lag

Co- integrat

ed (Yes/N

o)

Yes/ NO

Direction of causality

34 HERHON 1692 2 No Yes SP causes EXR

35 HEXAWARE 1588 6 No Yes SP causes EXR

36 HINDALCO 1687 6 No Yes SP causes EXR

37 Hinduja TMT 1693 2 No Yes SP causes EXR

38 HLL 1693 6 No Yes SP causes EXR

39 Hotel Leela 1687 7 No No

40 ICICI 1687 8 Yes Yes Bi- Directional

41 I-Flex Solutions 1043 2 No Yes SP causes EXR

42 Indian Hotels 1689 6 No Yes Bi- Directional*

43 Indian Oil 1693 7 No Yes SP causes EXR

44 INFOSYS 1689 8 No Yes EXR Causes SP

45 IPCL 1693 8 No Yes SP causes EXR

46 ITC 1687 8 No No

47 Jet Airways 1687 8 Yes No

48 Jindal Saw 1687 6 No Yes SP causes EXR

49 Jindal Stainless 681 2 No Yes SP causes EXR

50 JSW Steel 1668 2 No No

51 Jubilant Organ. 1500 6 No No

52 K E C International 79 1 No No

53 L & T 1262 6 No Yes SP causes EXR

54 Lupin 1632 2 No Yes SP causes EXR

55 M & M 1689 6 No Yes SP causes EXR

56 M I C O 1687 6 No Yes EXR Causes SP

57 Mangalore Ref. 1687 4 No Yes SP causes EXR

58 MARUTI UDYYOG 776 2 No Yes SP causes EXR

59 Matrix Labs. 1687 6 No Yes EXR Causes SP

60 Micro Inks 1687 2 No Yes SP causes EXR

61 Moser Baer (I) 1686 6 No No

62 MPhasis BFL 1603 3 No No

63 MTNL 6 No Yes SP causes EXR 1689

64 NALCO 1684 2 No No SP causes EXR

65 NTPC 430 2 No No SP causes EXR

66 OBC 1689 6 No Yes

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Bi- Directional

Table 6.4.11.1 continued..

Causality SL. No Name of the

company

No.of Observations

Lag Co-

integrated

(Yes/No) Yes/ NO

Direction of causality

67 ONGC 1692 3 Yes Yes Bi- Directional

68 Orchid Chemicals 1680 6 No Yes EXR Causes SP

69 Patni computers 611 2 No No

70 Petronet LNG 582 6 No Yes EXR Causes SP

71 PNB 1084 6 No Yes SP causes EXR

72 Punj Lloyd 125 1 Yes Yes EXR Causes SP

73 RANBAXY 1686 7 No Yes Bi- Directional

74 REL 1681 8 No No

75 RIL 1625 8 No Yes SP causes EXR

76 Rolta India 1687 6 No No

77 SAIL 1692 3 No Yes SP causes EXR*

78 Satyam 1626 8 No Yes SP causes EXR

79 SBI 1687 8 No Yes Bi- Directional

80 Sesa Goa 1687 8 No Yes SP causes EXR

81 Shree Renuka Sug 174 1 No No

82 SHIPPING 1692 2 No Yes SP causes EXR

83 SIEMENS 1684 3 No No

84 SpiceJet 1687 6 No No

85 Sterling Biotech 1640 6 No No

86 Sterlite Inds. 1286 10 No No

87 Sun Pharma 1689 6 No Yes EXR Causes SP

88 SUZLON 180 1 No No

89 Tata Chemicals 1689 6 Yes Yes Bi- Directional*

90 Tata Motors 654 2 No Yes SP causes EXR

91 TATA POWER 1687 6 No Yes SP causes EXR

92 TATA TEA 1689 6 No No

93 TCS 481 2 No No

94 TISCO 1628 6 No Yes SP causes EXR*

95 United Phosphates 1693 2 No No

96 VSNL 1687 6 No No

97 WIPRO 6 No Yes Bi- Directional 1627

98 Wockhardt 2 Yes No 1634

99 ZEE TELE 1692 3 No Yes SP causes EXR

* at 10% significance . All others at 5% significance Source: Data analysis Detailed test results in appendix 6.4.11 in softcopy on the CD-ROM

188

At the micro level individual companies responded differently to the

exchange rate fluctuations and no conclusive patterns could be seen in the behavior of

the share prices. Only seven companies indicated some sort of long term relationship

between the share price and the exchange rate and forty two companies indicated short

run relationship with exchange rate changes. This is evidenced by the co integration

and causality test results indicated above table 6.4.11.1. Detailed test results are

appended in Annexure 6.4.11.in soft copy form

6.4.11.2 In order to identify whether there was any special impact on the firms,

dependent on their multinational characteristics, some companies belonging to BSE

200 were included in the study , based on whether they were

Export intensive - Foreign exchange inflow is more than the foreign exchange

outgo(48 companies). Import intensive - whose revenue expenses are substantively import based and

the foreign exchange outgo is more than the foreign exchange earned (30

companies). Domestic - where the net foreign exchange earnings or outgo is either nil or

negligible and whose earnings and expenses are substantively in Rupees (22

companies). Accordingly the sample consisted of 48 companies which were Export

intensive, 30 import intensive and 22 in the domestic category. To get further insights,

the results from table 6.4.11.1 were cross tabulated based on the above categorization,

and is presented in table 6.4.11.2 below.

189

TABLE 6.4.11.2 Impact of Exchange Rate on Individual Company's Share Price categorised by multinational characteristics - SUMMARY

CAUSALITY Number of companies \

Category TOTAL Co-

integratedOnly

EXR to SP

Only SP to EXR

Both Bi -

Directional NONE

Domestic 21 1 0 13 4 4

Export intensive 5 5 48 4 16 22

30 4 3 14 2 11 Import intensive

99 9 8 43 11 37 Total

Percentage under each category 100.0% 9.1% 8.1% 43.4% 11.1% 37.4%

Source: Data Analysis : Based on the results of the test of co-integration and causality of daily exchange rate and share prices . Refer 6.4.11.1above and 'Tables for chapters, in the folder appendix in the soft copy (CD ROM).

On the whole, 54(43+11) companies had causality running from Stock

prices to Exchange rate, 19(8+11) companies with causality from exchange rate to

Stock prices, 11 companies with two way causality and 37 with no causality, depicting

the short term dynamics and 9 companies indicating long- term co- movements.

According to the ‘goods market approach10’ it is generally believed that

exchange rate competitiveness affects export return and hence has impact on the

value and on the share price of export intensive companies. However, this was true

only in case of ten (5 +5) companies, accounting for just above 20% in the export

intensive category in the selected sample (Table 6.4.11.2).

10 Dornbusch and Fisher , 1980

190

On the whole, across the various categories also, approximately 20% of the

companies had impact from exchange rate fluctuations. This included four banks in

the domestic category and two import intensive companies with Bi- directional causality

(EXR SP).

In the import intensive category of 30 companies, only five (3+2)

companies’ share prices had any impact from exchange rate fluctuations and for

about 53% of the companies stock prices were leading the short term influence.

From the above test results, it appears that the impact of exchange rate

fluctuations was company specific and possibly depended on the foreign exchange

exposure and the risk management strategies adopted by each company. In case of

companies for which hedging was incomplete, it affected the performance results and

the market discounted such factors on a gross basis. When the changes in exchange

rate was gradual such information did not appear to be market sensitive. It was also

seen that even for some of those companies with substantive foreign exchange

exposure, there was no statistically significant impact on the share prices from the

exchange rate fluctuations.

The chapter has provided a detailed report on the extensive search for the

Impact of exchange fluctuations on the share prices in the Indian capital market. It

has been indeed difficult to tame the vagaries of the market into a rigid model which is

statistically perfect fitting. However, the results of the various tests when analyzed in

detail tell volumes about the realities of the market and relevance of various policy

regimes.

191